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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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An Efficient Real-Time NMPC for Quadrotor Position control under Communication Time-Delay 16

An Efficient Real-Time NMPC for Quadrotor Position Control u...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Carlos, Barbara Barros Sartor, Tommaso Zanelli, Andrea Frison, Gianluca Burgard, Wolfram Diehl, Moritz Oriolo, Giuseppe Sapienza Univ Roma Dept Informat Automat & Management Engn DIAG Rome Italy Katholieke Univ Leuven Dept Mech Engn MECO Grp Leuven Belgium Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Germany Univ Freiburg Dept Comp Sci IIF Freiburg Germany

ISBN: (纸本)9781728177090

the advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors. these systems are characterized by their underactuation, nonlinearities, bounded inputs, and time-delays. Classical control solutions fall short in overcoming these difficulties and fully exploiting the capabilities offered by such platforms. this paper presents the design and implementation of an efficient position controller for quadrotors based on real-time NMPC with time-delay compensation and bounds enforcement on the actuators. To deal with the limited computational resources onboard, an offboard control architecture is proposed. It is implemented using the high-performance software package acados, which solves optimal control problems and implements a real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme with Gauss-Newton Hessian approximation. the quadratic subproblems (QP) in the SQP scheme are solved with HPIPM, an interior-point method solver, built on top of the linear algebra library BLASFEO, finely tuned for multiple CPU architectures. Solution times are further reduced by reformulating the QPs using the efficient partial condensing algorithm implemented in HPIPM. We demonstrate the capabilities of our architecture using the Crazyflie 2.1 nano-quadrotor.

关键词： Time delay

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the Design of Cathode Copper Quality Inspection System Based on Machine vision

The Design of Cathode Copper Quality Inspection System Based...

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robotics, control and automation Engineering (RCAE), international conference on

作者： Juan Lin Yuan Yuan Yongjing Wang School of Electric Engineering Lanzhou Institute of Technology Lanzhou Gansu China

ISBN: (数字)9781665459419

ISBN: (纸本)9781665459426

in order to meet the market requirements for the surface quality of cathode copper, a copper plate surface quality detection system based on machine vision is designed. the surface quality of cathode copper is judged by image acquisition and processing. According to the actual production conditions of a cathode copper production workshop in Guangxi and the characteristics of the cathode copper itself, the hardware equipment related to the Jetson nano and camera are selected to ensure that the images that can meet the processing requirements are collected. the Yolo algorithm is used to train the target detection, feature extraction and target recognition on the Linux system to obtain a data set. the data set trained by the computer is run on the Jeston nano micro controller, After the CSI camera collects the image data, the data is compared and displayed on the LCD1602. the detection system can increase the efficiency to automatically judge whether the product quality is qualified, solve the problems of low manual detection accuracy and large amount of labor, and realize the full-automatic production of cathode copper.

关键词： Target recognition Machine vision Production Cameras Product design Quality assessment Copper

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Examining the Effectiveness of a Robotic-Human-Machine-Interface on Sleepiness During Highway Automated Driving 15th

Examining the Effectiveness of a Robotic-Human-Machine-In...

引用

15th international conference on Social Computing and Social Media, SCSM 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

作者： Karatas, Nihan Yoshihara, Yuki Tanabe, Hiroko Tanaka, Takahiro Fujikake, Kazuhiro Takeuchi, Shuhei Iwata, Kenji Harazawa, Makoto Kamiya, Naoki Institute of Innovation for Future Society Nagoya University Aichi Japan School of Psychology Chukyo University Aichi Japan Tokai Rika Co. Ltd. Aichi Japan

ISBN: (纸本)9783031359262

the automation of vehicles is increasing and is expected to become a part of our daily lives in the near future. Furthermore, the progress in automated driving operations will change the driver’s role to that of a passive monitor. However, a lack of active involvement in driving situations and monotonous driving environments increase driver sleepiness. Considering the low automation levels in vehicles (i.e., Levels 2 and 3), drivers’ arousal levels and ability to monitor the road environment are critical. In this study, we examined the effectiveness of a robotic-human-machine-interface (RHMI) in alleviating the sleepiness level of drivers through random news information presentation. We conducted a within-subject experiment with 12 participants in a driving simulator environment and compared three conditions: no information was provided (the control condition);information was provided through a voice-only interface;and information was provided through an RHMI prototype. the qualitative and quantitative results of this experiment revealed that the information provision interfaces (voice-only and RHMI) had the potential to alleviate the sleepiness level in Level 2 highway automated driving. In addition, the RHMI prototype alleviated sleepiness and improved the arousal of drivers more than in the voice-only and no-information provision conditions. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： robotics

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Multi-objective Optimization Layout of Intelligent Robot Integrated Workshop Based on Machine vision 22

Multi-objective Optimization Layout of Intelligent Robot Int...

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Proceedings of the 2022 4th international conference on robotics, Intelligent control and Artificial Intelligence

作者： Lina Guo Applied Electronics Department shandong institute commerce and technology China

ISBN: (纸本)9781450398343

the application object of layer manufacturing unit is mainly intelligent factory or robot manufacturing unit with high degree of automation. Compared with the traditional single layer manufacturing unit, the number of equipment of multi-layer manufacturing unit is more, and the three-dimensional space factor needs to be considered, so the problem is more complicated. this paper mainly studies the multi-objective optimization layout of intelligent robot integrated workshop based on machine vision. In this paper, based on the proposed DCSGSA algorithm, according to the computational complexity and multi-scale characteristics of the integrated model, a multi-layer optimization strategy of robot work unit layout is designed to solve the model. the proposed algorithm was used to solve a practical case of intelligent die casting island parts taking robot unit of a die casting machine company, and compared with other multi-objective algorithms, the efficiency of the proposed algorithm was verified.

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Self-intersection Attention Pooling Based Classification for Rock Recognition 16

Self-intersection Attention Pooling Based Classification for...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Chen, Liang Zheng, Qian Liu, Zhitao Mao, Weijie Lin, Fulong Zhejiang Univ State Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China China Railway Engn Equipment Grp Co LTD Gen Inst Design & Res Zhengzhou Peoples R China

ISBN: (纸本)9781728177090

Rock recognition is a critical step for intelligent control system in tunnel boring machine (TBM). We build the rock recognition dataset by the visual monitoring system in TBM. After that, we present an attention-fusion network (AFN) for rock recognition classification. Our model is trained with the recognition object (rock) image, and gets the rock attention map through the self-intersection operation. To complete the classification task, we propose the attention block in the attention-fusion classifier, which is effective to fuse and encode the feature and attention in an end-to-end manner. We confirm the effectiveness of the self-intersection operation and attention block by visualized experiment and contrast experiment. At the same time, experimental results of the rock recognition dataset indicate that AFN outperforms state-of-the-art methods, including LSTM (mask-guided), ABN (attention-based), ResNet50 (global-feature), both in accuracy and real-time on the rock recognition tasks.

关键词： Rocks

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A Real-Time Monocular vision-Based Obstacle Detection 6

A Real-Time Monocular Vision-Based Obstacle Detection

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6th international conference on control, automation and robotics (ICCAR)

作者： Wang, Szu-Hong Li, Xiang-Xuan Natl Yunlin Univ Sci & Technol Bachelor Program Interdisciplinary Studies Touliu 640 Yunlin Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn Touliu 640 Yunlin Taiwan

ISBN: (纸本)9781728161396

In recent years, many robotic obstacle avoidance methods use high-priced laser detection technology, or a dual-lens camera as a sensor for robot control. In order to reduce costs, this paper presents a real-time obstacle detection system by using a single wide-angle camera. In the robot application, the system can detect various static or dynamic objects in front of the robot body. the proposed system uses a local object based background subtraction method to get the outline of the static/moving object. the adaptive integral image based obstacle detection method without manual adjustment of setting parameters. the obstacle detection system had been implemented by ARM system, the obstacle can be detected on the real-time camera. the frame rate is close to 32 fps with Raspberry pi 3 platform.

关键词： obstacle detection robot monocular vision

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Double Closed Loop controller with Current Sharing of Interleaved DC/DC Converter for Dynamic Wireless Power Transfer System 16

Double Closed Loop Controller with Current Sharing of Interl...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Chen, Wenjie Liu, Zhitao Su, Hongye Zhang, Liyan Zhejiang Univ Inst Cyber Syst & Control Fac State Key Lab Ind Control Technol Hangzhou Peoples R China Wuhan Univ Technol Fac Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781728177090

Dynamic wireless power transfer (DWPT) of electric vehicles (EVs) is considered as an economic and effective solution on traffic electrification. However, the coupling coefficient between the transmitting and receiving coils changes dynamically with the movement of EV, resulting in a sharp drop in energy transfer power and efficiency. In this case, the double closed loop controller with current sharing of interleaved DC/DC converter is proposed to against coupling coefficient variation. And two layers double D coil structure is presented to make the distribution of magnetic density and temperature more uniform. Moreover, Simulation studies are carried out in PLECS to verify the proposed strategy in DWPT system. Results show that the proposed algorithm can not only significantly improve the energy transfer power and efficiency, but also ensure the current sharing of interleaved DC/DC converter, so as to enhance the robustness and stability of DWPT system.

关键词： Electric vehicles (EVs) dynamic wireless power transfer (DWPT) coupling coefficient interleaved DC/DC converter doubled closed loop controller current sharing

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Fish Keypoints Detection for Ecology Monitoring Based on Underwater Visual Intelligence 16

Fish Keypoints Detection for Ecology Monitoring Based on Und...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Suo, Feiyang Huang, Kangwei Ling, Gui Li, Yanjun Xiang, Ji Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310015 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728177090

this paper introduces a fishery ecology monitoring system for cultivation pools, and proposes a new stereo keypoint detection method followed by curve fitting analysis to estimate the fish posture and length. the system, which can be employed for aquaculture monitoring, is featured by its exploitation of underwater visual intelligence and deep neural-network architecture. As input, stereo image pairs are obtained by underwater binocular camera. A deep neural-network under Faster R-CNN architecture is built to detect fish from the stereo image inputs. Another network under Stacked Hourglass architecture is constructed to detect specific keypoints of each fish. For ecology monitoring, detected keypoints are used in the estimation of the fishes' posture and length. Unlike other size estimation methods which also apply a binocular camera, our method naturally bypasses the pixel-wise matching difficulty in global stereo matching algorithms. Experiment shows that our system is applicable for online fish ecology monitoring, with efficient and accurate estimation performance.

关键词： Fish

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A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries 16

A State-of-Charge Estimation Method based on Bidirectional L...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Zhang, Zhen Xu, Ming Ma, Longhua Yu, Binchao Zhejiang Univ Collage Control Sci & Engn CSE Hangzhou Peoples R China Zhejiang Univ Inst Automat & Elect Engn Ningbo Inst Technol Ningbo Peoples R China Zhejiang Zheneng Nat Gas Operat Co Hangzhou Peoples R China

ISBN: (纸本)9781728177090

State-of-charge (SOC) estimation is the key to the BMS of lithium-ion batteries. At present, data-driven methods are more popularly used to estimate the SOC. Among them, the deep learning technology especially recurrent neural networks (RNNs) performed very well. In this paper, we proposed a bidirectional long short-term memory (Bi-LSTM) neural network to perform SOC estimation. the bidirectional LSTM layer is employed to catch the forward and backward temporal dependencies of battery sequential states, and shows better performance in SOC estimation of LiBs. With a public dataset of vehicle driving cycles, we separately train our proposed network at three different constant temperatures (0 degrees C, 10 degrees C, and 25 degrees C), and the evaluated MAEs are 0.498%, 0.411%, 0.738%. Besides, the Bi-LSTM network trained with overall datasets at three temperatures achieves a mean absolute error (MAE) of 0.616% and maximum absolute error (MaxE) of 3.809%. It shows that the proposed method is robust and accurate in SOC estimation.

关键词： Long short-term memory

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Fusion of Computer vision Method and Fin Ray Effect Soft Robotic Gripper for Fruit Harvesting

Fusion of Computer Vision Method and Fin Ray Effect Soft Rob...

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IEEE/ASME international conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Estêvão Vale Filho David Alves Ana Marques Alves Enrico Zardini Vinícius Cordeiro Matilde Galvão Martim Lima de Aguiar Rodrigo Antunes Nuno Pereira Pedro Dinis Gaspar University of Beira Interior Covilhã Portugal C-MAST - Centre for Mechanical and Aerospace Science and Technologies Covilhã Portugal University of Brescia Brescia Italy

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

As the demand for food production increases due to population growth, the agricultural sector has turned to automation to address labor shortages, enhance productivity, and promote sustainability. Autonomous robots are now employed in various tasks, including planting, production forecasting, pest and weed control, and harvesting fruits and vegetables. this article focuses on the use of harvesting robots to automate the collection of fallen fruits, specifically oranges, in orchards. Harvesting these fallen fruits can mitigate pest infestations, reduce the need for pesticides, and provide a source of animal feed. In this study, two new technologies were applied: computer vision and automated robotics, to develop a non-destructive system for fruit harvesting. this system is designed to be easy to implement and economically favorable for use in fruit and vegetable processing lines, guaranteeing fruit detection and picking oranges from the ground. the system was developed using a Raspberry Pi 5 to capture images of the fruit with a PiCamera module 3. An XZ gantry was used to position the gripper within the XZ plane for picking the oranges. One object detection model was developed using the TensorFlow Object Detection API and then converted into TensorFlow Lite to identify oranges using deep learning techniques. the model for detecting oranges achieved a mean Average Precision (mAP) of 98.31%, for Intersection over Union (IoU) [0.5:0.65] and 90.29% for (IoU) [0.65:0.80]. In the end, the results of the field trials were satisfactory: out of 100 trials, 87 resulted in complete captures, in which all three fingers of the gripper grasped the fruit evenly. In 13 cases, the tong grasped the fruit with only two fingers, while the third finger adjusted the fruit.

关键词： Computer vision Fingers Robot vision systems Wheels Object detection Cameras Mobile robots Object recognition Grippers Robots

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